AI coding assistants have changed the way developers work.
A developer can describe a feature, generate a function, fix an error, or even create an entire component in a few seconds. It can save a lot of time.
But there is an important question that doesn't get enough attention:
What happens when AI-generated code contains a security problem?
The answer is simple: the developer is still responsible for the code.
AI Can Write Working Code That Isn't Secure
One of the interesting things about AI-generated code is that it can look completely reasonable.
A function may work exactly as expected while still creating a security weakness.
For example, an AI assistant might generate code that:
Trusts user input too much
Handles authentication incorrectly
Exposes sensitive information in error messages
Uses insecure defaults
Stores secrets in the wrong place
Gives users more access than they should have
The problem isn't necessarily that AI "doesn't know security."
The problem is that generated code needs to be reviewed in the context of the actual application.
Security Review Should Become Part of the Workflow
Instead of thinking:
AI generated the code, so the code is finished.
A better workflow is:
Generate → Review → Test → Secure → Deploy
AI can help with the first step, but security requires human judgment.
Developers should understand what the generated code is doing before adding it to a production application.
Look Beyond Syntax
A normal code review often asks questions like:
Does the code work?
Is it readable?
Is it efficient?
Does it follow the project's style?
A security review asks additional questions:
Can an attacker control this input?
What happens if authentication fails?
Can another user access this resource?
Is sensitive information exposed?
What permissions does this function require?
What happens when unexpected data is supplied?
These questions are where cybersecurity becomes part of everyday development.
AI Can Also Help With Security
This doesn't mean developers should avoid AI coding tools.
Quite the opposite.
AI can be useful for:
Explaining unfamiliar code
Finding potential security issues
Suggesting validation improvements
Generating test cases
Reviewing repetitive code
Explaining security concepts
But AI should be treated as an assistant, not the final security authority.
The Developer Still Needs to Understand the Code
This is probably the most important lesson.
If you cannot explain what a piece of generated code does, you probably shouldn't deploy it yet.
The more AI becomes part of software development, the more valuable security awareness becomes.
Writing code is becoming easier.
Understanding why that code is safe is still a human responsibility.
*A Simple Rule
*
Before deploying AI-generated code, ask:
"If someone deliberately tries to misuse this feature, what could go wrong?"
That one question can lead to a surprisingly useful security review.
AI can accelerate development.
It shouldn't accelerate mistakes.
Developers who combine AI productivity with strong cybersecurity practices will be in a much better position as software development continues to change.
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